Elsevier

Early Childhood Research Quarterly

Volume 47, 2nd Quarter 2019, Pages 472-486
Early Childhood Research Quarterly

Family-centered measures of access to early care and education

https://doi.org/10.1016/j.ecresq.2018.08.001Get rights and content

Highlights

  • We introduce new family-centered measures of access for supply, cost and quality.

  • New access measures based on travel time include cross-boundary travel.

  • Areas designated as low access differ from conventional availability measures.

  • Families in Minnesota incur 36% higher costs to access highly rated child care.

Abstract

This study proposes new family-centered measures of access to early care and education (ECE) services with respect to quantity, cost, and quality and uses them to assess disparities in access across locations and socio-demographic groups in Minnesota. These measures are distance-based and use available geographic data to account for the fact that families can cross arbitrary administrative boundaries, such as census tract or ZIP code lines, and thus better reflect the real experiences of families than conventional area-based measures. Combining synthetic family locations simulated from Census demographic and geographic data and information on ECE provider locations, we calculate travel time between the locations of families with young children and ECE providers to measure families’ access to providers of different types. The results yield a map of areas with low and high relative ECE access. The average family in Minnesota lives in a location where there are nearly two children for every nearby slot of licensed capacity, however, access to ECE supply varies considerably at the local level. The supply measure can also serve as a weight useful in computing family-centered measures of ECE quality and access costs, incorporating both prices and travel costs, to further characterize the local ECE market from the perspective of families. Improving the measures of variation in families’ access to ECE quantity, cost, and quality is valuable as policymakers consider expansions to public supports for early learning and ECE entrepreneurs decide where to invest.

Introduction

Access to affordable, high-quality early care and education (ECE) is important because of the dual role of ECE in facilitating employment of parents and in supporting child development. As our understanding of the importance of early childhood learning environments expands, disparities in access to high-quality ECE have become of increasing concern to policymakers. The reauthorization of the Child Care and Development Block Grant (CCDBG) in 2014 established new requirements for states to improve access to high quality ECE (US DHHS, 2014). Studies that examine community-level measures of the availability of ECE conclude that disparities in access are related to the sociodemographic and economic characteristics of local communities (Bassok, Fitzpatrick, & Loeb, 2011; Bassok & Galdo, 2016; Fischer, Nelson, Mikelbank, & Coulton, 2008; Fuller & Liang, 1996; Gordon & Chase-Lansdale, 2001; Loeb, Fuller, Kagan, & Carrol, 2004).

To ensure adequate access to early care and education, particularly for low-income families, policymakers and planners need accurate measures of access in order to target resources to locations with greatest need. After a comprehensive review of the literature, Friesen, Lin, Forry, and Tout (2017) write, “Recent federal policy changes have placed new requirements on states to demonstrate and document their efforts to improve access to high-quality early care and education (ECE), and have made clear the urgent need for a shared understanding of this concept.” Many factors influence families’ access to ECE services. Past studies of access have mostly relied on area-based measures of the availability of ECE options. They typically partition a region into non-overlapping zones, using ZIP code, county, census tract, or municipal boundaries for example. It is common to compare a measure of supply, such as the number of providers, provider capacity, or a count of child care workers, to the number of children within each area (Bassok et al., 2011; Casper & O’Connell, 1998; Cochi Ficano, 2006; Fischer et al., 2008; Fuller & Liang, 1996; Gordon & Chase-Lansdale, 2001; Jacobson, 2001; Neidell & Waldfogel, 2009; Yamauchi, 2010). By defining relevant areas based on administrative boundaries, these conventional measures ignore families’ ability to travel across boundaries to find care. They implicitly consider all supply in the same area as a family as equally accessible to that family and all supply outside the area as completely inaccessible. In addition, standard area-based measures treat all families in the area as equally likely to demand all in-area supply and all families outside the area as irrelevant, when in reality, families will cross area administrative boundaries to find care.

This paper builds on the prior literature by developing a new set of family-centered, distance-based measures of access to ECE services’ quantity, cost, and quality. We measure the quantity of supply provided near where families with young children live and the number of other young children living nearby those providers potentially competing for the supply, the travel time required to reach that supply, and the cost and quality of the supply. We use Census data on the population of Minnesota families with young children combined with data on all the state’s licensed center and family child care providers, public preschool, and Head Start providers. Our distance-based measures do not implicitly restrict families from crossing administrative boundaries in accessing nearby ECE services. Our supply (quantity) measure uses an enhanced two-stage floating catchment area design (Luo & Qi, 2009), which formalizes the idea that greater access for a family means having more supply closer by with fewer other children potentially competing for that supply. It captures variation in the nearby supply of ECE services from families’ perspectives adjusting for the child population nearby each provider, where “nearby” is determined by drive time.

We extend the logic of this approach to propose new measures of families’ cost of access to ECE services and the quality of ECE services accessible to families. The cost-based measures reflect both the monetary price of care and the cost of travel between the family’s location and each nearby provider. The quality measure is basically a family-specific average of local provider quality. Each of these dimensions of access – quantity, cost, and quality – is different and important. Together, they give a holistic, textured view of ECE services from families’ perspectives. While additional factors influence families’ selection of ECE providers, such as the hours of care (for example, whether evening or weekend hours are needed and offered), the cultural and language fit, and even whether the family has information about the provider, these are beyond the scope of this study (Carrillo, Harknett, Logan, Luhr, & Schneider, 2017; Davis & Connelly, 2005; Han, 2004; Kim & Fram, 2009; Liang, Fuller, & Singer, 2000; Peyton, Jacobs, O’Brien, & Roy, 2001; Radey & Brewster, 2007).

These new methods measure access to ECE services at a particular location, characterize it across subpopulations of families with young children, and enable examination of disparities in access for different groups of families defined by combinations of geography, racial and ethnic background, and income level. We demonstrate this approach using data from Minnesota in 2015 to estimate these measures among different communities in the state and make comparisons across different kinds of families.

Section snippets

Literature review

A number of studies have shown how child care supply varies across communities in ways associated with community characteristics. Notably, child care availability is lower in rural areas and in lower-income communities compared to urban and higher-income areas (Cochi Ficano, 2006; Gordon & Chase-Lansdale, 2001; Hofferth & Wissoker, 1992). Other studies have found disparities across communities in the availability of preschool options (Bassok & Galdo, 2016), particularly high-quality settings (

Data on child care providers

Data on ECE providers in Minnesota in 2015 were obtained from the NACCRRAware data system managed by Child Care Aware of Minnesota (a child care resource and referral network). Information on each provider in the dataset includes total licensed capacity, enrollment by age group, prices by age group, and street address. The dataset identifies four types of providers: (i) child care centers (CCC), (ii) licensed family child care (FCC), (iii) Head Start and Early Head Start programs, and (iv)

Quantity: adjusted supply

Table 2 shows the results for adjusted supply. The average family with a young child in Minnesota lives in a location with slightly more than half a slot nearby (0.56) adjusting for the number of other young children nearby each slot. More simply, the average family has access to about half a slot per child. Turning this around, there are nearly two nearby children for every slot of capacity. Although this phrasing sounds similar to that referring to an area-based, slots-per-tot measure, the

Discussion and conclusion

This study, using GIS-based simulation methods, introduces new measures of access that are family-centered and reflect the quantity, cost and quality dimensions of access to early care and education services. The literature has acknowledged that the lack of data sources is the biggest impediment to the study of community-level child care accessibility (Gordon & Chase-Lansdale, 2001). Measures such as number of slots per child per county or number of child care workers per child within a 20–30

Acknowledgements

This work was supported by the University of Minnesota Faculty Grant-in-Aid program and the Center for Urban and Regional Affairs (CURA) Faculty Interactive Research Program. Davis also acknowledges the support of the USDA National Institute of Food and Agriculture, Hatch Multistate Project NE-1049 and Minnesota Agricultural Experiment Station Project MIN-14-081. The funding sources had no involvement in the study design, collection, analysis and interpretation of the data, writing of the

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